Awesome
[UPDATED] A TensorFlow Implementation of Attention Is All You Need
When I opened this repository in 2017, there was no official code yet. I tried to implement the paper as I understood, but to no surprise it had several bugs. I realized them mostly thanks to people who issued here, so I'm very grateful to all of them. Though there is the official implementation as well as several other unofficial github repos, I decided to update my own one. This update focuses on:
- readable / understandable code writing
- modularization (but not too much)
- revising known bugs. (masking, positional encoding, ...)
- updating to TF1.12. (tf.data, ...)
- adding some missing components (bpe, shared weight matrix, ...)
- including useful comments in the code.
I still stick to IWSLT 2016 de-en. I guess if you'd like to test on a big data such as WMT, you would rely on the official implementation. After all, it's pleasant to check quickly if your model works. The initial code for TF1.2 is moved to the tf1.2_lecacy folder for the record.
Requirements
- python==3.x (Let's move on to python 3 if you still use python 2)
- tensorflow==1.12.0
- numpy>=1.15.4
- sentencepiece==0.1.8
- tqdm>=4.28.1
Training
- STEP 1. Run the command below to download IWSLT 2016 German–English parallel corpus.
bash download.sh
It should be extracted to iwslt2016/de-en folder automatically.
- STEP 2. Run the command below to create preprocessed train/eval/test data.
python prepro.py
If you want to change the vocabulary size (default:32000), do this.
python prepro.py --vocab_size 8000
It should create two folders iwslt2016/prepro and iwslt2016/segmented.
- STEP 3. Run the following command.
python train.py
Check hparams.py to see which parameters are possible. For example,
python train.py --logdir myLog --batch_size 256 --dropout_rate 0.5
- STEP 3. Or download the pretrained models.
wget https://dl.dropbox.com/s/4lom1czy5xfzr4q/log.zip; unzip log.zip; rm log.zip
Training Loss Curve
<img src="fig/loss.png">Learning rate
<img src="fig/lr.png">Bleu score on devset
<img src="fig/bleu.png">Inference (=test)
- Run
python test.py --ckpt log/1/iwslt2016_E19L2.64-29146 (OR yourCkptFile OR yourCkptFileDirectory)
Results
- Typically, machine translation is evaluated with Bleu score.
- All evaluation results are available in eval/1 and test/1.
| tst2013 (dev) | tst2014 (test) |
|---|---|
| 28.06 | 23.88 |
Notes
- Beam decoding will be added soon.
- I'm going to update the code when TF2.0 comes out if possible.